Newsletter Archive

📅 Today’s Picks

Faster Table Joins with Polars Multi-Threading

Problem
pandas processes joins on a single CPU core, leaving other cores idle during large table operations.
Solution
Polars distributes join operations across all available CPU cores, achieving significantly faster joins than pandas on large datasets.
What makes Polars fast:

Processes rows in parallel batches
Uses all available CPU cores
Zero configuration required

📖 View Full Article

🧪 Run code

⭐ View GitHub

🔄 Worth Revisiting

Faster Polars Queries with Programmatic Expressions

Problem
When you want to use for loops to apply similar transformations, each Polars with_columns() call processes sequentially.
This prevents the optimizer from seeing the full computation plan.
Solution
Instead, generate all Polars expressions programmatically before applying them together.
This enables Polars to:

See the complete computation plan upfront
Optimize across all expressions simultaneously
Parallelize operations across CPU cores

📖 View Full Article

🧪 Run code

⭐ View GitHub

☕️ Weekly Finds

Mole
[Python Utils]
– Deep clean and optimize your Mac with a simple command-line tool.

marker
[LLM]
– Convert PDF, DOCX, PPTX, and other documents to markdown with high speed and accuracy.

pathway
[Data Engineer]
– Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Looking for a specific tool? Explore 70+ Python tools →

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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🔄 Worth Revisiting

Build Professional Python Packages with UV –package

Problem
Python packages turn your code into reusable modules you can share across projects.
But building them requires complex setup with setuptools, managing build systems, and understanding distribution mechanics.
Solution
UV, a fast Python package installer and resolver, reduces the entire process to 2 simple commands:

uv init –package sets up your package structure instantly
uv build and uv publish to create and distribute to PyPI

📖 Learn more

⭐ View GitHub

Generate Time-Sortable IDs with Python 3.14’s UUID v7

Problem
UUID4 generates purely random identifiers that lack chronological ordering.
Without embedded timestamps, you need separate timestamp fields and custom sorting logic to organize records by creation time.
Solution
Python 3.14 introduces UUID version 7 with built-in timestamp ordering.
Key features:

Determine creation order by comparing two UUIDs directly
Retrieve exact creation time by extracting the embedded timestamp

☕️ Weekly Finds

smolagents
[LLM]
– A barebones library for agents that think in code

rembg
[ML]
– A tool to remove images background

Scrapegraph-ai
[LLM]
– Python scraper based on AI

Looking for a specific tool? Explore 70+ Python tools →

📚 Latest Deep Dives

Visualize Machine Learning Results with Yellowbrick
– Learn to visualize ML model performance with Yellowbrick. Create confusion matrices, ROC curves, and feature importance plots in scikit-learn pipelines.

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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🔄 Worth Revisiting

Python 3.14: Type-Safe String Interpolation with t-strings

Problem
Building SQL queries with f-strings directly embeds user input into the query string, allowing attackers to inject malicious SQL commands.
Parameterized queries are secure but require you to maintain query templates and value lists separately.
Solution
Python 3.14 introduces template string literals (t-strings). Instead of returning strings, they return Template objects that safely expose interpolated values.
This lets you validate and sanitize interpolated values before building the final query.

🧪 Run code

Build Self-Documenting Regex with Pregex

Problem
Regex patterns like [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} are difficult to read and intimidating.
Team members without regex expertise might struggle to understand and modify these validation patterns.
Solution
Team members without regex expertise might struggle to understand and modify these validation patterns.
Pregex transforms regex into readable Python code using descriptive components.
Key benefits:

Code that explains its intent without comments
Easy modification without regex expertise
Composable patterns for complex validation
Export to regex format when needed

📖 View Full Article

🧪 Run code

⭐ View GitHub

☕️ Weekly Finds

MindsDB
[LLM]
– AI data automation solution that connects and unifies enterprise data for real-time decision-making.

MarkItDown
[Python Utils]
– Lightweight Python utility for converting various files to Markdown for use with LLMs.

Reflex
[Python Utils]
– Open-source framework empowering Python developers to build web apps faster in a single language.

Looking for a specific tool? Explore 70+ Python tools →

📚 Latest Deep Dives

Visualize Machine Learning Results with Yellowbrick
– Learn to visualize ML model performance with Yellowbrick. Create confusion matrices, ROC curves, and feature importance plots in scikit-learn pipelines.

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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📅 Today’s Picks

PySpark 4.0: Query Nested JSON Without StructType

Problem
Extracting nested JSON in PySpark requires defining StructType inside StructType inside StructType. This creates verbose, inflexible code that breaks when your JSON structure changes.
Solution
PySpark 4.0’s Variant type lets you skip schema definitions entirely. All you need is parse_json() to load and variant_get() to extract with JSONPath.
Key benefits:

No upfront schema definition
Handle any nesting depth with simple $.path syntax
Schema changes don’t break your code
Extract only the fields you need, when you need them

📖 View Full Article

🧪 Run code

⭐ View GitHub

☕️ Weekly Finds

toon
[LLM]
– Compact, human-readable JSON encoding for LLM prompts with schema-aware Token-Oriented Object Notation

cocoindex
[Data Processing]
– Ultra performant data transformation framework for AI with incremental processing

sqlfluff
[Data Engineer]
– Modular SQL linter and auto-formatter with support for multiple dialects and templated code

Looking for a specific tool? Explore 70+ Python tools →

📚 Latest Deep Dives

Visualize Machine Learning Results with Yellowbrick
– Learn to visualize ML model performance with Yellowbrick. Create confusion matrices, ROC curves, and feature importance plots in scikit-learn pipelines.

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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📅 Today’s Picks

Stream Large CSVs to Parquet with Polars sink_parquet

Problem
Traditional workflows load the full CSV into memory before writing, which crashes when the file is too large.
Solution
Polars sink_parquet() streams data directly from CSV to Parquet without loading the entire file into memory.
Instead of load-then-write, sink_parquet uses read-write-release:

Reads a chunk from CSV
Writes it to Parquet
Releases memory before next chunk
Repeats until complete

📖 View Full Article

🧪 Run code

⭐ View GitHub

Codon: One Decorator to Turn Python into C Speed

Problem
Slow Python functions in large codebases are painful to optimize. You might try Numba or Cython, but Numba only works for numerical code with NumPy arrays.
You might try Cython, but it needs .pyx files, variable type annotations, and build setup. That’s hours of refactoring before you see any speedup.
Solution
Codon solves this with a single @codon.jit decorator that compiles your Python to machine code.
Key benefits:

Works on any Python code, not just NumPy arrays
No type annotations required since types are inferred automatically
Compiled functions are cached for instant repeated calls
Zero code changes beyond adding the decorator

🧪 Run code

⭐ View GitHub

☕️ Weekly Finds

metabase
[Data Viz]
– Open-source Business Intelligence and Embedded Analytics tool that lets everyone work with data

Surprise
[ML]
– Python scikit for building and analyzing recommender systems with SVD, KNN, and more algorithms

highdimensional-decision-boundary-plot
[Data Viz]
– Scikit-learn compatible approach to plot high-dimensional decision boundaries for intuitive model understanding

Looking for a specific tool? Explore 70+ Python tools →

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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📅 Today’s Picks

Build a Simple Portfolio Analyzer in Python with ffn

Problem
If you have ever wanted a simple way to analyze your investment portfolio as a side project, you know how tedious it is to piece together multiple Python libraries.
Solution
ffn consolidates the entire portfolio analysis workflow into one package with a Pandas-like API.
Core features:

Fetch stock prices directly from Yahoo Finance
Calculate returns and risk metrics automatically
Find the best allocation across your assets
Plot performance comparisons and correlations

🧪 Run code

⭐ View GitHub

Analyze GitHub Repositories with LangChain Document Loaders

Problem
Are you tired of manually searching through hundreds of GitHub issues with keyword search to find what you need?
Solution
With LangChain’s GitHubIssuesLoader, you can load repository issues into a vector store and query them with natural language instead of exact keywords.
You can ask questions like “What feature requests are related to video?” and get instant, relevant answers from your issue history.

📖 View Full Article

🧪 Run code

⭐ View GitHub

☕️ Weekly Finds

PlotNeuralNet
[Data Viz]
– LaTeX code for drawing publication-quality neural network diagrams for reports and presentations

yellowbrick
[ML]
– Visual analysis and diagnostic tools for machine learning with scikit-learn integration

TPOT
[MLOps]
– Python Automated Machine Learning tool that optimizes ML pipelines using genetic programming

Looking for a specific tool? Explore 70+ Python tools →

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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🤝 COLLABORATION

Data Contracts: Developing Production Grade Pipelines at Scale
Poor data quality can cause major problems for data teams, from disrupting pipelines to losing consumer trust. Many teams struggle with this, especially when data comes from upstream workflows outside their control.
The solution: data contracts. They document expectations, establish ownership, and enforce constraints within CI/CD workflows.
This practical book introduces data contract architecture, explains why the industry needs it, and shares real-world production use cases. You’ll learn to implement components and build a case for adoption in your organization.

→ Try Chapter 7 in your browser

📅 Today’s Picks

Build Visual Tables with Great Tables Nanoplots

Problem
Data tables with raw numbers lack visual context.
You can’t spot trends or patterns at a glance when looking at columns of digits.
Solution
Great Tables’ fmt_nanoplot() embeds mini line or bar charts directly into table cells.
Key features:

Transform numeric series into scannable visualizations
Customize colors and styles for data points and lines
Switch between line plots and bar charts
Add data area shading for emphasis

📖 View Full Article

🧪 Run code

⭐ View GitHub

☕️ Weekly Finds

TabPFN
[ML]
– Foundation model for tabular data with zero-shot classification and regression capabilities

scikit-survival
[ML]
– Survival analysis built on top of scikit-learn for time-to-event prediction

dedupe
[Data Processing]
– Python library for fuzzy matching, record deduplication and entity resolution using machine learning

Looking for a specific tool? Explore 70+ Python tools →

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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📅 Today’s Picks

LangChain v1.0: Auto-Protect Sensitive Data with PIIMiddleware

Problem
User messages often contain sensitive information like emails and phone numbers.
Logging or storing this data without protection creates compliance and security risks.
Solution
LangChain v1.0 introduces PIIMiddleware to automatically protect sensitive data before model processing.
PIIMiddleware supports multiple protection modes:

5 built-in detectors (email, credit card, IP, MAC, URL)
Custom regex for any PII pattern
Replace with [REDACTED], mask as ****1234, or block entirely

📖 View Full Article

🧪 Run code

⭐ View GitHub

Test File Operations Without Risk Using tmp_path

Problem
Testing file operations requires touching the actual file system, which can be dangerous if not handled carefully. Real data can be overwritten by mistake.
Tests can also leave behind unwanted files across your project.
Solution
The tmp_path fixture provides a safe alternative by creating temporary, isolated directories that clean up themselves after each test.
Here’s how to use tmp_path:

Add tmp_path to your test function signature
Work with it like any pathlib.Path object
pytest handles the rest: isolated directories per test, automatic cleanup

📖 Learn more

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☕️ Weekly Finds

quarkdown
[Python Utils]
– Modern Markdown-based typesetting system that compiles projects into print-ready books or interactive presentations with live preview and fast compilation

slim
[MLOps]
– Container optimization tool that makes Docker images 10-30x smaller without changing your development workflow

shapiq
[ML]
– Python package for approximating Shapley interactions and explaining feature interactions in machine learning model predictions

Looking for a specific tool? Explore 70+ Python tools →

📚 Latest Deep Dives

Great Tables: Publication-Ready Tables from Polars and Pandas DataFrames
– Turn Polars and Pandas DataFrames into professional tables with automatic number formatting, visual heatmaps, and sparkline charts. Fully reproducible when data updates.

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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📅 Today’s Picks

dataclass vs Pydantic Field(): Declarative Constraints

Problem
dataclass requires manual validation in __post_init__, separating validation rules from field definitions.
As your data model grows, the __post_init__ method fills with if-else statements, becoming harder to read and maintain.
Solution
Pydantic Field() puts constraints directly on field definitions, making your model self-documenting and easier to maintain.
What you can specify with Field():

Numeric bounds (e.g., age must be >= 0 and

📅 Today’s Picks

Delta Lake: Sync DataFrames with One Line of Code

Problem
Updating an entire table with millions of rows just to fix a handful of records is costly and unnecessary.
Solution
Delta Lake’s when_matched_update() modifies only the matching rows, leaving unchanged data untouched.
Delta Lake also gives you:

Atomic updates that fully succeed or fully roll back
Partial file rewrites instead of full dataset copies
Time travel to restore previous versions

📖 View Full Article

🧪 Run code

⭐ View GitHub

📢 ANNOUNCEMENTS

New Way to Explore Python Tools on CodeCut
CodeCut just became a lot more searchable! ☕️
Most developers stick to the same 5-10 Python libraries. Not because others aren’t useful, but because finding them is hard.
CodeCut now organizes 69 tools into eight categories on the homepage:

Developer tools
AI frameworks
Data processing
Databases
Python builtins
Visualization
Utilities
Text processing

Each tool links directly to blogs and code snippets. Browse a new category today. You might find something that changes how you work.
What would make CodeCut more useful for you? Reply and let me know. I’m always looking for ways to improve it.

→ Explore Tools

☕️ Weekly Finds

deepdoctection
[ML]
– Python library for document extraction and layout analysis using deep learning models

postgresus
[Data Engineer]
– Self-hosted PostgreSQL backup and monitoring tool with web UI

ffn
[Data Processing]
– Financial functions library for quantitative finance in Python

Looking for a specific tool? Explore 70+ Python tools →

Stay Current with CodeCut

Actionable Python tips, curated for busy data pros. Skim in under 2 minutes, three times a week.

.codecut-subscribe-form .codecut-input {
background: #2F2D2E !important;
border: 1px solid #72BEFA !important;
color: #FFFFFF !important;
}
.codecut-subscribe-form .codecut-input::placeholder {
color: #999999 !important;
}
.codecut-subscribe-form .codecut-subscribe-btn {
background: #72BEFA !important;
color: #2F2D2E !important;
}
.codecut-subscribe-form .codecut-subscribe-btn:hover {
background: #5aa8e8 !important;
}

.codecut-subscribe-form {
max-width: 650px;
display: flex;
flex-direction: column;
gap: 8px;
}
.codecut-input {
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
background: #FFFFFF;
border-radius: 8px !important;
padding: 8px 12px;
font-family: ‘Comfortaa’, sans-serif !important;
font-size: 14px !important;
color: #333333;
border: none !important;
outline: none;
width: 100%;
box-sizing: border-box;
}
input[type=”email”].codecut-input {
border-radius: 8px !important;
}
.codecut-input::placeholder {
color: #666666;
}
.codecut-email-row {
display: flex;
align-items: stretch;
height: 36px;
gap: 8px;
}
.codecut-email-row .codecut-input {
flex: 1;
}
.codecut-subscribe-btn {
background: #72BEFA;
color: #2F2D2E;
border: none;
border-radius: 8px;
padding: 8px 14px;
font-family: ‘Comfortaa’, sans-serif;
font-size: 14px;
font-weight: 500;
cursor: pointer;
text-decoration: none;
display: flex;
align-items: center;
justify-content: center;
transition: background 0.3s ease;
}
.codecut-subscribe-btn:hover {
background: #5aa8e8;
}
.codecut-subscribe-btn:disabled {
background: #999;
cursor: not-allowed;
}
.codecut-message {
font-family: ‘Comfortaa’, sans-serif;
font-size: 12px;
padding: 8px;
border-radius: 6px;
display: none;
}
.codecut-message.success {
background: #d4edda;
color: #155724;
display: block;
}
@media (max-width: 480px) {
.codecut-email-row {
flex-direction: column;
height: auto;
gap: 8px;
}
.codecut-input {
border-radius: 8px;
height: 36px;
}
.codecut-subscribe-btn {
width: 100%;
text-align: center;
border-radius: 8px;
height: 36px;
}
}

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